The Impact of Foreign Direct Investment and R&D Expenditures on Climate Change: The Case Of BRICS-T and Selected OECD Member Countries
Bibliographic record
Abstract
Climate change, one of the most urgent problems of today's world, is an important issue emphasized by researchers and policymakers. Climate change, which has become a global problem, has negative consequences on the environment and people. Therefore, the main objective of this study is to investigate the impact of FDI and R&D expenditures on climate change in BRICS-T (Brazil, Russia, India, China, South Africa, Turkey) and selected OECD (United Kingdom, Italy, Canada, Spain, Portugal, Denmark, Norway, Austria, Spain, Portugal, Denmark, Norway, Australia) member countries for the period 2000-2020 using panel data analysis method. OECD and BRICS-T country groups are analyzed separately. According to the results of the analysis, it is found that there is no causality relationship between climate change and research and development variables, while there is a bidirectional causality relationship between research and development and foreign direct investment variables. When the causality relationship is analyzed for countries and variables, it is found that there is a bidirectional causality relationship between research and development and FDI variables for Russia and India, and a unidirectional causality relationship from research and development variable to FDI variable for China, South Africa and Turkey.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".